pyFTS.models.incremental package

Module contents

FTS methods with incremental/online learning

Submodules

pyFTS.models.incremental.Retrainer module

Meta model that wraps another FTS method and continously retrain it using a data window with the most recent data

class pyFTS.models.incremental.Retrainer.Retrainer(**kwargs)

Bases: pyFTS.common.fts.FTS

Meta model for incremental/online learning

auto_update = None

If true the model is updated at each time and not recreated

forecast(data, **kwargs)

Point forecast one step ahead

Parameters:

• data – time series data with the minimal length equal to the max_lag of the model
• kwargs – model specific parameters

Returns:

a list with the forecasted values

fts_method = None

The FTS method to be called when a new model is build

fts_params = None

The FTS method specific parameters

model = None

The most recent trained model

partitioner = None

The most recent trained partitioner

partitioner_method = None

The partitioner method to be called when a new model is build

partitioner_params = None

The partitioner method parameters

train(data, **kwargs)

Method specific parameter fitting

Parameters:

• data – training time series data
• kwargs – Method specific parameters

window_length = None

The memory window length